Papers › GraphSAINT: Graph Sampling Based Inductive Learning Method
GraphSAINT: Graph Sampling Based Inductive Learning Method
Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, Viktor Prasanna
Graph Convolutional Networks (GCNs) are powerful models for learning representations of attributed graphs. To scale GCNs to large graphs, state-of-the-art methods use various layer sampling techniques to alleviate the "neighbor explosion" problem during minibatch training. We propose GraphSAINT, a graph sampling based inductive learning method that improves training efficiency and accuracy in a fundamentally different way. By changing perspective, GraphSAINT constructs minibatches by sampling the training graph, rather than the nodes or edges across GCN layers. Each iteration, a complete GCN is built from the properly sampled subgraph. Thus, we ensure fixed number of well-connected nodes in all layers. We further propose normalization technique to eliminate bias, and sampling algorithms for variance reduction. Importantly, we can decouple the sampling from the forward and backward propagation, and extend GraphSAINT with many architecture variants (e.g., graph attention, jumping connection). GraphSAINT demonstrates superior performance in both accuracy and training time on five large graphs, and achieves new state-of-the-art F1 scores for PPI (0.995) and Reddit (0.970).
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Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Link Property Prediction | ogbl-citation2 | GraphSAINT (GCN aggr) | Ext. data | No | #19 of 23 | Archive leaderboard | report |
| Link Property Prediction | ogbl-citation2 | GraphSAINT (GCN aggr) | Number of params | 296449 | #19 of 23 | Archive leaderboard | report |
| Link Property Prediction | ogbl-citation2 | GraphSAINT (GCN aggr) | Test MRR | 0.7985 ± 0.0040 | #19 of 23 | Archive leaderboard | report |
| Link Property Prediction | ogbl-citation2 | GraphSAINT (GCN aggr) | Validation MRR | 0.7975 ± 0.0039 | #19 of 23 | Archive leaderboard | report |
| Node Classification | PPI | GraphSAINT | F1 | 99.50 | #3 of 24 | Archive leaderboard | report |
| Node Classification | GraphSAINT | Accuracy | 97.0% | #6 of 16 | Archive leaderboard | report | |
| Node Property Prediction | ogbn-mag | GraphSAINT + metapath2vec | Ext. data | No | #28 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT + metapath2vec | Number of params | 309764724 | #28 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT + metapath2vec | Test Accuracy | 0.4966 ± 0.0022 | #28 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT + metapath2vec | Validation Accuracy | 0.5066 ± 0.0017 | #28 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT (R-GCN aggr) | Ext. data | No | #30 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT (R-GCN aggr) | Number of params | 154366772 | #30 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT (R-GCN aggr) | Test Accuracy | 0.4751 ± 0.0022 | #30 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-mag | GraphSAINT (R-GCN aggr) | Validation Accuracy | 0.4837 ± 0.0026 | #30 of 39 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT-inductive | Ext. data | No | #46 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT-inductive | Number of params | 331661 | #46 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT-inductive | Test Accuracy | 0.8027 ± 0.0026 | #46 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT-inductive | Validation Accuracy | Please tell us | #46 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT (SAGE aggr) | Ext. data | No | #51 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT (SAGE aggr) | Number of params | 206895 | #51 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT (SAGE aggr) | Test Accuracy | 0.7908 ± 0.0024 | #51 of 64 | Archive leaderboard | report |
| Node Property Prediction | ogbn-products | GraphSAINT (SAGE aggr) | Validation Accuracy | 0.9162 ± 0.0008 | #51 of 64 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
Introduced by this paper: GraphSAINT
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